Francesca Zaffora Blando (Stanford) is a PhD candidate in Philosophy and Symbolic Systems at Stanford University, where she is a recipient of the Patrick Suppes Fellowship in Philosophy of Science. She also holds an MA in Philosophy (Edinburgh) and an MSc in Logic (ILLC, Amsterdam). Her PhD dissertation, supervised by Johan van Benthem (Stanford, Amsterdam, Tsinghua), centres around the interconnections between algorithmic randomness—a branch of computability theory—and various formal models of learning, including formal learning theory and Bayesian epistemology. In a recent paper, A learning-theoretic characterisation of Martin-L¨of randomness and Schnorr randomness (currently under review), she uses notions from formal learning theory to provide novel characterisations of standard algorithmic randomness notions in terms of unlearnability. Her most recent project, in collaboration with Simon Huttegger (UC Irvine) and Sean Walsh (UCLA), focuses on applying algorithmic randomness to classical Bayesian convergence-to-the-truth [Huttegger, 2015] and merging-of-opinions results [Blackwell and Dubins, 1962, Gaifman and Snir, 1982]. The authors consider computable versions of these classical results and provide precise characterisations of the collection of environments in which computable Bayesian agents are inductively successful using algorithmic randomness notions. More specifically, they show that the random worlds, or environments, are exactly the ones in which a certain kind of inductive success is attainable. For more information, please visit her website.